The present invention relates to an image processing system and more particularly to an edge detection method and apparatus of an image processing system using multistate linear threshold logic elements.
In a motion detection system, which is one of the most important application areas of the present invention, is preprocessing is recommended because it can improve the accuracy in detecting motion vectors by removing unwanted factors in the image such as noise. Preprocessing can also reduce the computational load by mapping a full resolution image, usually represented by 8 bits per pixel, to a different type image represented by less than 8 bits per pixel.
Several kinds of preprocessing have been proposed for the above mentioned purpose. For example, Band Extract Representative Point (BERP) Method which was used by Umori et. al in "Automatic Image stabilizing system by Full-Digital Signal Processing", on IEEE Transactions on Consumer Electronics Vol. 36, No.3 published in Aug., 1990, can be thought of as a kind of bandpass filtering method. Although BERP method can efficiently filter out both the extremely high spatial frequency components such as noise and the low frequency components such as the flat area in intensity, it still needs more than two bits per pixel to represent the BERP image. The detected edge information can be represented by one bit per pixel. Accordingly, the edge detection method can simplify the hardware. There are various approaches for edge detection such as: (i) the use of image spatial gradients, (ii) the use of the Laplacian, (iii) the use of differences of averages, (iv) matching or fitting to a prespecified pattern, and (v) the detection of the zero-crossings filtered by the Laplacian of the Gaussian (LOG) in the image. The two major drawbacks of the edge detectors enumerated in (i)-(iv) are that they perform well only with some images and that performance of edge detection is prominently degraded when noise is present. On the other hand, approach (v) reduces the noise effect through the convolution with a Gaussian-shaped kernel. This operation may also improve the connectivity of extracted image and it guarantees that the zero crossings of the second derivative are preserved. However, the LOG operator has the potential drawback that the mask size is not constant and it becomes very large with a large amount of noise.